Training Data Generation for Artificial Intelligence-Based Sequencing

2. The computer-implemented method of claim 1, further including training a classifier based upon the cluster map, the classifier being a neural network-based template generator for processing input image data to generate a decay map, a ternary map, or a binary map, representing one or more properties of each of a plurality of clusters represented in the input image data for base calling by a neural network-based base caller.

4. The computer-implemented method of claim 1, wherein the cluster map identifies boundaries between contiguous subpixels whose base call sequences do not substantially match.

5. The computer-implemented method of claim 1, wherein generating the cluster map comprises identifying as background those subpixels that do not belong to any of the disjointed regions.

6. The computer-implemented method of claim 1, wherein the cluster map identifies cluster boundary portions between two contiguous subpixels whose base call sequences do not substantially match.

8. The non-transitory computer readable memory of claim 7, further including instructions that, when executed by the at least one processor, cause the system to implement actions of training a classifier based upon the cluster map, the classifier being a neural network-based template generator for processing input image data to generate a decay map, a ternary map, or a binary map, representing one or more properties of each of a plurality of clusters represented in the input image data for base calling by a neural network-based base caller.

10. The non-transitory computer readable memory of claim 5, wherein the cluster map identifies boundaries between contiguous subpixels whose base call sequences do not substantially match.

11. The non-transitory computer readable memory of claim 7, further including instructions that, when executed by the at least one processor, cause the system to implement actions comprising generating the cluster map by identifying as background those subpixels that do not belong to any of the disjointed regions.

12. The non-transitory computer readable memory of claim 7, wherein the cluster map identifies cluster boundary portions between two contiguous subpixels whose base call sequences do not substantially match.

15. The computer-implemented system of claim 14, further including instructions that, when executed by the at least one processor, cause the computer-implemented system to implement actions of training a classifier based upon the cluster map, the classifier being a neural network-based template generator for processing input image data to generate a decay map, a ternary map, or a binary map, representing one or more properties of each of a plurality of clusters represented in the input image data for base calling by a neural network-based base caller.

17. The computer-implemented system of claim 14, wherein the cluster map identifies boundaries between contiguous subpixels whose base call sequences do not substantially match.

18. The computer-implemented system of claim 14, further including instructions that, when executed by the at least one processor, cause the computer-implemented system to implement actions comprising generating the cluster map by identifying as background those subpixels that do not belong to any of the disjointed regions.

19. The computer-implemented system of claim 14, wherein the cluster map identifies cluster boundary portions between two contiguous subpixels whose base call sequences do not substantially match.